4.1 Demographic Profile Table 4.1: Description of the Sample Sample (n = 200) Variable Percentage

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CHAPTER 4 RESULTS

This chapter focuses on reporting and analyzing the data collected and the statistical results for this study. Multiple regression analysis is adopted as the main multivariate technique to test the relationship between the independent and dependent variables. In this chapter, the findings from the research are presented.

4.1 Demographic Profile

Table 4.1: Description of the Sample Sample (n = 200)

Variable Percentage (%)

________________________________________________________________________

Age 20 – 29 20 30 – 39 46.5 40 – 49 32

≥50 1.5

Gender Male 44

Female 56 Monthly Income (RM) Less than 2,000 1 2,001 – 4,000 19 4,001 – 6,000 20.5 6,001 – 8,000 24.5 8,001 – 10,000 21.5 More than 10,000 13.5

Current job position Top management 0 Senior Manager 22.5 Department/Assistant Manager 42 Skilled professional 7.5 Executive 22 Others 6

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Years in Current Employment Less than a year 9

Between 1 and 5 years 30.5 Between 6 and 10 years 38 Between 11 and 20 years 14.5 More than 20 years 8

Highest Education level Secondary 0

Diploma 1

Degree 79

Post-graduate 20 ________________________________________________________________________

Almost half of the respondents fell into the 30-39 years age group and almost equal proportion of male and female. In terms of monthly income, more than half were in the range of RM4001- RM10000.

The respondents are from various job positions with majority being Department/Assistant Manager. More than two-thirds of the respondents have working experience between 1- 10 years in the current employment and almost all were educated with at least a degree or postgraduate qualification.

4.2 Goodness of measure

SPSS software version 16 was used to test the measurement scales reliability, examine the validity of the theoretical framework and test the hypothesized relationships. The internal consistency of the measurement scale used to measure employee recognition, employee engagement, organizational commitment, job satisfaction and turnover intention was measured using Cronbach’s Alpha.

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4.2.1 Factor analysis

Factor Analysis test was conducted on the variables. The questionnaire was factor analyzed using the Principal Component Analysis procedure in order to determine the structure of the variables. Bartlett’s test of sphericity is significant and the Kaiser-Meyer- Olkin measure of sampling adequacy is 0.802 which indicate the factorability of the matrix as a whole. The Kaiser ruling is to drop all components with eigenvalues less than 1.0.

A minimum of 3 items per factor is critical ( McDonald & Krane, 1977; Rindskopf, 1984). Communalities should be greater than 0.6 and the mean level of communality should be at least 0 .7 (MacCallum et al., 1999). In high communalities, recovery of population factors in sample data is normally very good regardless of sample size or the presence of model error (MacCallum & Hong, 2001).The sample-to-population pattern fit is very good for the high loading condition (0.80), moderate for the middle (0.60) loading condition, and very poor for low loading (0.40) condition (Velicer & Fava, 1998).

4.2.1.1 Factor analysis for Independent Variables

Bartlett’s test of sphericity is significant and the Kaiser-Meyer-Olkin measure of sampling adequacy is 0.797. The eigenvalues suggest 7 factors to be extracted based on eigenvalues greater than 1. The 7 factors explained the 77% of the variance. (Refer Appendix 2)

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The 23 items were analyzed and 9 items were dropped based on low Anti-image value, cross- loading and low communalities value (Refer Appendix 5). Removal of the 9 items resulted with 4 factors from 7 factors initially as stated in Table 4.2.

Table 4.2: Factor loadings on Independent variables after dropping items Factors

1 2 3 4

No. Items

EE OC MPR ER EE2 When I get up in the morning, I feel like

going to work. 0.836 0.397 0.107

EE3 My job inspires me. 0.868 0.181 0.205

OC2

I really feel as if this organization's

problems are my own. 0.857 0.121 0.190

OC9

Even if it were to my advantage, I do not feel it would be right to leave my organization now.

0.804 0.443 0.119

EE5 I get carried away when I’m working. 0.742 0.160 0.105

OC4 Too much of my life would be disrupted if I decided I wanted to leave my

organization right now.

0.254 0.922 0.129

OC10 This organization deserves my loyalty. 0.267 0.921

EE1 I am enthusiastic about my job. 0.387 0.892

ER3 I am not satisfied with the

management’s participation and role in my recognition.

0.962

ER6 The employee recognition process is a valuable tool for showing gratitude and recognizing performance.

0.958

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ER1 I am satisfied with verbal recognition from my supervisor.

0.683

ER5 Receiving recognition for my job motivates me to improve my performance.

0.121 0.881

ER2 When I do a good job, I receive the recognition that I should.

0.129 0.855

ER4

I am satisfied with the type and value of the rewards presented (example:

luncheons, service pins, nonmonetary gifts).

0.778

Eigenvalues 5.54 2.40 1.96 1.35

Percentage of variance 39.57% 17.15% 14.01% 9.66%

KMO Measure of Sampling Adequacy 0.782

Bartlett’s Test of Sphericity, Approx. Chi-Square = 3133.000 and significant, p< 0.05

Based on the items that were loaded on each factor, the 4 factors were renamed. The first factor is named employee engagement (EE) as the items describe on the extent of an employee’s commitment, work efforts, enthusiasm for work and desire to stay with an organization The second factor is named organizational commitment (OC) as the items emphasizes on emotional attachment to the organization and employee’s commitment to the organization. The third factor is named management participation in recognition (MPR) as the items describe the role of management in recognition. The fourth factor is named as employee recognition (ER) as the items emphasis on receiving of recognition for performance and job well done.

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4.2.1.2 Factor loadings for Mediating variable

Bartlett’s test of sphericity is significant and the Kaiser-Meyer-Olkin measure of sampling adequacy is 0.657. The eigenvalues suggest 4 factors to be extracted based on eigenvalues greater than 1. The 4 factors explained the 73% of the variance (Refer Appendix 3).

The 11 items were analyzed and 6 items were dropped based on low Anti-image value, cross- loading and low communalities value (Refer Appendix 6). Removal of the 6 items resulted with 1 factor from 4 factors initially as stated in Table 4.3.

Table 4.3: Factor loadings on Mediating variable after dropping items Factor 1 No. Items

JS JS10 I am satisfied with my chances for promotion on

my job

0.879 JS3 I feel I am being paid a fair amount for the work I

do. 0.799

JS9 The benefits we receive are as good as most other organizations offer.

0.749

JS1 I am satisfied with my job 0.664

JS2 I don’t like doing the things I do at work. 0.644

Eigenvalues 2.828

Percentage of variance 56.56%

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4.2.1.3 Factor loadings for Dependent variable

Bartlett’s test of sphericity is significant and the Kaiser-Meyer-Olkin measure of sampling adequacy is 0.787. The eigenvalues suggest 2 factors to be extracted based on eigenvalues greater than 1. The 2 factors explained the 70% of the variance (Refer Appendix 4).

The 6 items were analyzed and 2 items were dropped based on low communalities value (Refer Appendix 7). Removal of the 2 items resulted with 1 factor from 2 factors initially as stated in Table 4.4.

Table 4.4: Factor loadings on Dependent variable after dropping items Factor 1 No. Items

TI TI5 I will stay with this organization for the foreseeable

future.

0.952 TI2 I would turn down an offer from another company

if it came tomorrow 0.939

TI3 I plan to be with this company five years from now. 0.826 TI1 I feel strongly that i will leave the organization

within next 12 months

0.773

Eigenvalues 3.067

Percentage of variance 76.67%

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4.2.1.4 Summary of Factor Analysis

Sixteen items were dropped after the factor analysis. A new conceptual framework and hypotheses will be developed to accommodate the results. All the measures of sampling adequacy are well above the acceptable level of 0.5 which indicate that the correlation matrix is suitable for factor analysis.

The data analysis conducted assessed the reliability of the variables. The acceptable reliability coefficient for variable is 0.7 and above but lower values sometimes can also be accepted (Nunnaly, 1978). All variables have a reliability above 0.70 which indicates good reliability as an instrument where the data is reliable and acceptable for further analysis. The internal consistency of reliability (coefficient alpha) for each variable is presented in Table 4.5.

Table 4.5: Reliability analysis for the variables

Variable

Cronbach’s

Alpha No. of Items

Employee engagement 0.94 5

Organizational commitment 0.96 3

Management participation in

recognition 0.85 3

Employee recognition 0.81 3

Job Satisfaction 0.80 5

Turnover Intention 0.89 4

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4.2.2 Revised Framework after Factor Analysis

The revised framework has four independent variables compared to the initial framework which has only three independent variables. The mediating variable and dependent variable remains the same as the initial framework.

Turnover Intention Employee

Engagement

Management participation in recognition

Job Satisfaction Organizational

Commitment Employee Recognition

Figure 4.1: Revised Theoretical Framework

4.2.3 Revised Hypotheses

The hypotheses were revised accordingly to accommodate the revised framework in Figure 4.1 and will be used for the testing of hypotheses in this study. Based on the factor analysis, the following hypotheses were developed:

H1: Job satisfaction will positively mediate the relationship between management participation in recognition and turnover intention.

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H2: Job satisfaction will positively mediate the relationship between employee engagement and turnover intention.

H3: Job satisfaction will positively mediate the relationship between organizational commitment and turnover intention.

H4: Job satisfaction will positively mediate the relationship between employee recognition and turnover intention.

4.2.4 Descriptive Statistic Analysis

Table 4.6: Mean, Standard Deviation and Correlation Mean Standard

Deviation TI JS MPR EE OC ER

TI 2.705 0.867 1

JS 4.182 1.044 -0.107 1

MPR 5.387 0.729 -0.068 0.174* 1

EE 5.374 0.866 -0.943* 0.088 0.05 1

OC 5.097 1.002 -0.779* 0.086 0.069 0.584* 1

ER 5.563 0.765 -0.232* 0.05 0.109 0.249* 0.181* 1

* Correlation is significant at the 0.01 level (1-tailed).

Employee recognition has the highest mean value of 5.563 among the variables and Turnover intention has the lowest mean value of 2.705, The Job satisfaction data has the largest variation among the variables as indicated by the standard deviation value of 1.044 and Management participation in recognition has the lowest data variation as indicated by the standard deviation value of 0.729.

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The high coefficient correlation value for Employee engagement and Organizational commitment, r = -943 and r = -0.779 respectively shown in Table 4.6 indicates there is a significant high negative correlation exist between both the independent variables and turnover intention. The coefficient correlation obtained for Employee recognition, r = -0.232 indicates significant low negative correlation with turnover intention. The nearer the correlation is to either +1 or -1, the stronger the correlation between the variables.

The magnitude and the direction of the correlation explain the relationship between variables.

4.2.5 Multiple Regression Analysis

Multiple regression analysis was performed to determine the relationship between the independent or predictor variables and the dependent variable. Regression analysis is used to establish an equation that will predict a dependent variable using one or more independent variables.

Multicollinearity is a condition of very high intercorrelations or inter-associations among the independent variables. Multicollinearity is a type of interruption in the data where it’s present in the data, the statistical inferences made about the data may not be reliable.

Multicollinearity can be detected with the tolerance values and variance inflation factor (VIF). If the value of tolerance is less than 0.1 and the value of VIF is 10 and above, then multicollinearity exist (Hair et al.,2006).

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Multicollinearity on independent variables was measured using Variance inflation Factor for all independent variables, the tolerance values were ranging from 0.610 to 0.986 and VIF values ranging from 1.015 to 1.639 which indicates no multicollinearity problem exist. Scatterplot test of linearity was found to be satisfactory (Refer Appendix 8).

Normality test using the normal probability plots indicates that cases falls in more or less in a straight line.

Multiple regression analysis was also conducted to test for the significance of Job Satisfaction as a mediator for the relationship between the independent variables and the dependent variable. Baron and Kenny (1986) detail the required conditions for a variable to function as a mediator. The test was done using the four step method proposed by Baron and Kenny (1986).The four steps are establish that IV is related to DV, establish that IV is related to Mediator, establish that Mediator is related to DV and establish that Mediator completely mediates the IV-DV relationship. Regression analysis results are presented in Table 4.7.

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Table 4.7: Regression results

Variable Std Beta

Step 1 Std Beta

Step 2 Std Beta Step 3 & 4 Independent variables

Employee engagement Organizational commitment

Management participation in recognition Employee recognition

-0.744^**

-0.347^**

-0.009 0.017

0.051 0.043 0.168^*

0.011

-0.743^**

-0.346^**

-0.007 0.017

Mediating variable

Job satisfaction -0.012 (Step 3)

F value R²

Adjusted R² R² Change F Change Sig. F Change

1497^**

0.968 0.968 0.968 1496.966^**

0.000

1.925 0.038 0.018 0.038 1.925 0.108

1197^**

0.969 0.968 0.969 1196.630^**

0.000 Note:^* p<0.05, ^** p < 0.01

The independent variables explain 96.8 per cent (Table 4.7) of the variance in turnover intention which is significant as indicated by the F-value of 1496.966, p< 0.01.

Employee engagement and Organizational commitment with beta value -0.744 and -0.347, p < 0.01 respectively indicates significant negative relationship with turnover intention. Management participation in recognition with beta value 0.168, p < 0.05 indicates significant positive relationship with Job Satisfaction.

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There was no significant relationship between the mediating variable and the dependent variable. This finding concludes that Job satisfaction does not mediate the relationship between the independent variables and turnover intention therefore Job satisfaction is not a mediator in this study.

4.2.5.1 Summary of hypotheses testing

Overall, the result of the findings does not support hypothesis H1, H2, H3 and H4. The results from the hypothesis tests are summarized in Table 4.8.

Table 4.8: Summary of the hypothesis results

Hypothesis Determination H1: Job satisfaction will positively mediate the

relationship between management participation in recognition and turnover intention.

Hypothesis not supported

H2: Job satisfaction will positively mediate the relationship between employee engagement and turnover intention.

H3: Job satisfaction will positively mediate the relationship between organizational commitment and turnover intention.

H4: Job satisfaction will positively mediate the relationship between employee recognition and turnover intention.